Understanding strategic bidding in multi-unit auctions: a case study of the Texas electricity spot market.

These tests indicate that the additive separability restriction holds on average across the bidders, and that there is heterogeneity across bidders in terms of how close they come to satisfying this restriction. The heterogeneity pattern affirms that the theoretical restriction might be a good approximation to reality; bidders who appear to perform well both from the ex post and ex ante profit maximization benchmarks (especially Reliant, who performs best) come close to satisfying additive separability.

5. Explaining deviations from optimal bidding

* This Section investigates explanations for the observed deviations from static profit maximization and the considerable heterogeneity across firms in terms of performance. We explore whether the observed deviations are driven by characteristics of the firms such as the firm size, the firm type (e.g., investor-owned utility versus municipal utility), and the generation technology. We find that the most significant determinant of performance is the size of the stakes that each firm has in the balancing market, which suggests there are scale economies to participation in the balancing market auctions. Finally, we document evidence of a modest degree of learning by the small firms.

[] Participation costs and scale economies in bidder performance. We consider the hypothesis that small firms might not have sufficiently large dollar stakes to justify the fixed cost of participating in the balancing market. To do this, we first have to clarify what we mean by "participation." We find that many firms forego profits because their bid functions have a large range of prices at which quantity offered is zero. For example, in Figure 2, Guadalupe is not offering to supply additional INC energy until the price reaches $33 nor offering to reduce production until price is $11 despite the fact that one of its units can INC and DEC at a marginal cost of $28. These bid patterns effectively price the bidders out of the market for plausible realizations of residual demand (in fact, as Table 1 shows, some firms at the bottom of this table are almost always priced out of the market). In separate calculations, we find that the six firms with the highest measures of Percent Achieved are called to produce balancing power in 67% of the actions. In contrast, the other firms are called to produce in only 31% of auctions, despite the fact that it is ex post optimal to produce in 89% of the auctions.

An interpretation of these bid patterns is that it does not pay for the small firms to bid the optimal markup even if this optimal markup would allow them to profit from incrementing/decrementing their power generation, or "participating" in the balancing market much more often. This interpretation is plausible if it is costly for these firms to calculate the optimal markup.

The fixed and variable costs of running a trading operation are likely not to be trivial. One market participant suggested that even a simple bidding operation would require an upfront expenditure of $3 million with annual operating costs of $1 million, and that most sophisticated trading operations could be much more expensive. The magnitude of these costs is not trivial compared to the "money-left-on-table" figures reported in Table 1. (30) Another component of this "fixed cost of participation" is institutional. ERCOT only allows certified companies, QSEs, to submit bids in the balancing market. All other firms have to route their bids through a QSE, or contract with a QSE to conduct their bidding operations. This suggests that only firms with greater dollar stakes may find it optimal to incur the fixed costs of becoming a QSE.

The presence of such fixed costs leads to substantial heterogeneities in bidding behavior--not just in outcomes, but also in the strategies that are being used. Many bidders do not make full use of the strategy space available to them, but rather use coarse-grained bidding strategies. The bid rules allowing 40 price-quantity points afford generators a large degree of flexibility in bidding. However, none of the bidders make full use of the 40 bid points that they can use to trace out their optimal bidding functions. Among the firms serving as their own QSE, the firm earning the greatest fraction of ex post profits (Reliant) also uses the largest number of bid points, averaging 22.2 points per bid schedule. None of the other firms use more than 13 points on average. Apparently, traders choose not to formulate refined bid strategies with desired quantities for many potential realizations of the market clearing price.

One explanation for such "coarse-grained" bidding strategies is provided by Kastl (2006a). In Kastl's model of bidding in Czech treasury auctions, there is an explicit marginal cost of submitting a price-quantity point. Thus, "coarse" bids are constrained optimal, and can depart significantly from bids that can comprise a larger number of points. Although the cost of adding bid points may explain a portion of the foregone ex post profits, it appears the majority of foregone profits is not due to bidding constraints. To see this, suppose that there were a cost to adding bid points that restricted firms to submitting the number of bid points that we observe them using (rather than 40). For example, TXU uses an average of 12.6 bid points. We calculate naive best-response (NBR) profits using 12 equally spaced prices between 10 and 40. Note that because the 12 price points are fixed rather than chosen optimally, we will understate best-response profits and thus overstate Percent Achieved. Even relative to this "constrained" benchmark, TXU's Percent Achieved is only 60%. Performing the same exercise for Calpine (using 7 points) yields a Percent Achieved of 45%. (31)

Even conditional on paying the fixed cost of becoming a QSE, scale economies still appear to matter. This is clearly seen in Figure 3, which displays the relationship between bidding performance and size for generation firms that act as their own QSE. Our measure of performance is the percent of ex post optimal profits, calculated in the manner described in Section 4. Our measure of stakes in the balancing market is the volume of sales under ex post optimal bidding (using other size measures, such as actual volume of sales, or firms' total capacity, yields similar patterns). There is a positive relationship between Percent Achieved and optimal sales volume. The figure includes the fitted linear relationship, which is positive and marginally significant when all firms are included. When Bryan is excluded, the relationship is even stronger and highly significant.

We now examine the extent to which stakes are correlated with performance for the broader sample of firms in a regression context, along with other firm-specific factors that we believe might affect performance. We regress each firm's measure of Percent Achieved on a measure of stakes in the balancing market--the volume of sales under ex post optimal bidding (SIZE). Also included are firm-level covariates on firm type (independent/merchant power producer, municipal utility, and investor-owned utility) and whether the firm acts as its own bidder (OWNBIDDER). Finally, we include dummy variables for whether the firm's generation technology is at least 50% comprised of two technologies that are less flexible to quick changes in output (COAL and COMBINED-CYCLE).

Results are reported in Table 4. The baseline regression in column 1 yields a result that is consistent with the "scale hypothesis:" a 1000 MW increase in sales is associated with a 52 percentage point increase in Percent Achieved.

Column 2 suggests that a "corporate governance"-based explanation is not borne out by the data. Controlling for size and technology, the performance of municipal utilities appears to be slightly (2.5 %-4.8%) better than that of investor-owned utilities, although the regression coefficient is not statistically significant at conventional levels. Moreover, merchant firms actually seem to be the weakest performers.

[FIGURE 3 OMITTED]

We also find that the technology mix of a firm does not appear to affect its performance on the balancing market. In column 3, we add measures of technology type and find that owning a large fraction of coal and combined-cycle generation units does not negatively impact performance.

We can best measure scale effects if we focus on those firms that choose to establish their own bidding operation rather than those that outsource. In column 4, we control for whether the firm performs its own bidding and allow the effect of SIZE to vary by OWNBIDDER status. Larger stakes are associated with higher performance for firms that serve as their own bidders. For firms that perform their own bidding, a 1000 MW increase in optimal sales volume is associated with an 86 percentage point increase in Percent Achieved.

Moreover, if one were to view the choice to serve as their own bidder as a revealed preference, then the threshold volume of sales where it becomes profitable to construct an in-house bidding operation rather than to outsource is 71 MW (= (-.071/.001)). The results are similar when we control for firm type and technology (column 5). A 1000 MW increase in optimal sales volume is associated with an 97 percentage point increase in Percent Achieved and the threshold size for ownbidding is 163 MW.